# Real-time goal recognition using approximations in Euclidean space

arXiv: https://arxiv.org/abs/2307.07876 · HTML: https://mlsanity.com/paper/2307.07876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Plan Explicability and Predictability for Robot Task Planning](https://mlsanity.com/paper/1511.08158/) (2015)
- [Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search](https://mlsanity.com/paper/1707.01888/) (2017)
- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [Optimal Cost Design for Model Predictive Control](https://mlsanity.com/paper/2104.11353/) (2021)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)

## Built on by (most cited first, 3 of 3)

- [General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning](https://mlsanity.com/paper/2505.09737/) (2025)
- [Online Goal Recognition using Path Signature and Dynamic Time Warping](https://mlsanity.com/paper/2605.07736/) (2026)
- [Zero-Shot Goal Recognition with Large Language Models](https://mlsanity.com/paper/2605.15333/) (2026)

## Abstract (arXiv)

While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.